U.S. patent application number 17/108367 was filed with the patent office on 2021-12-30 for drug recommendation method and device, electronic apparatus, and storage medium.
This patent application is currently assigned to Beijing Baidu Netcom Science and Technology Co., Ltd.. The applicant listed for this patent is Beijing Baidu Netcom Science and Technology Co., Ltd.. Invention is credited to Haifeng Huang, Chao Lu, Zhenhui Shi, Yuan Xia.
Application Number | 20210407642 17/108367 |
Document ID | / |
Family ID | 1000005264691 |
Filed Date | 2021-12-30 |
United States Patent
Application |
20210407642 |
Kind Code |
A1 |
Xia; Yuan ; et al. |
December 30, 2021 |
DRUG RECOMMENDATION METHOD AND DEVICE, ELECTRONIC APPARATUS, AND
STORAGE MEDIUM
Abstract
A drug recommendation method and device, an electronic
apparatus, and a storage medium are provided, which are related to
the fields of artificial intelligence deep learning technology,
intelligent recommendation, and knowledge graph. The specific
implementation includes: acquiring related information of a target
object; and determining drug recommendation information for the
target object based on the related information of the target object
and a first model, where the drug recommendation information
contains information of at least one drug, where the first model is
a model obtained by performing iterative processing on output
information of a second model, and the second model is used for
evaluating drug recommendation information output by the first
model during the iterative processing, to obtain an evaluation
result of the drug recommendation information.
Inventors: |
Xia; Yuan; (Beijing, CN)
; Shi; Zhenhui; (Beijing, CN) ; Lu; Chao;
(Beijing, CN) ; Huang; Haifeng; (Beijing,
CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Beijing Baidu Netcom Science and Technology Co., Ltd. |
Beijing |
|
CN |
|
|
Assignee: |
Beijing Baidu Netcom Science and
Technology Co., Ltd.
Beijing
CN
|
Family ID: |
1000005264691 |
Appl. No.: |
17/108367 |
Filed: |
December 1, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16H 20/10 20180101;
G16H 70/40 20180101; G06F 40/205 20200101; G16H 10/60 20180101 |
International
Class: |
G16H 20/10 20060101
G16H020/10; G16H 70/40 20060101 G16H070/40; G16H 10/60 20060101
G16H010/60; G06F 40/205 20060101 G06F040/205 |
Foreign Application Data
Date |
Code |
Application Number |
Jun 24, 2020 |
CN |
202010589770.X |
Claims
1. A drug recommendation method, comprising: acquiring related
information of a target object; and determining drug recommendation
information for the target object based on the related information
of the target object and a first model, wherein the drug
recommendation information contains information of at least one
drug, wherein the first model is a model obtained by performing
iterative processing on output information of a second model, and
the second model is used for evaluating drug recommendation
information output by the first model during the iterative
processing, to obtain an evaluation result of the drug
recommendation information.
2. The drug recommendation method according to claim 1, further
comprising: obtaining drug recommendation information for a
patient, based on medical record information of the patient to be
trained and a pre-trained first model; obtaining an evaluation
result of the drug recommendation information for the patient based
on the second model, wherein the evaluation result indicates
whether there is incompatibility in the drug recommendation
information for the patient; and determining whether training of
the first model is completed based on the evaluation result.
3. The drug recommendation method according to claim 2, wherein the
determining whether the training of the first model is completed
based on the evaluation result comprises: updating the training of
the first model and updating training of the second model, to
obtain an updated-trained first model and an updated-trained second
model, in response to determining that the evaluation result
indicates there is incompatibility in the drug recommendation
information for the patient; re-obtaining drug recommendation
information for the patient by using the updated-trained first
model and the medical record information of the patient to be
trained, and re-obtaining an evaluation result of the drug
recommendation information for the patient based on the
updated-trained second model; and determining that the training of
the first model is not completed, until the evaluation result
indicates that there is no incompatibility in the drug
recommendation information for the patient.
4. The drug recommendation method according to claim 3, wherein the
obtaining the evaluation result of the drug recommendation
information for the patient based on the second model comprises:
evaluating a drug combination output by the pre-trained first model
based on the second model, to obtain a first reward value
corresponding to the drug combination and a probability value of
incompatibility between drugs in the drug combination, and taking
the first reward value and the probability value as the evaluation
result.
5. The drug recommendation method according to claim 4, wherein the
obtaining the drug recommendation information for the patient based
on the medical record information of the patient to be trained and
the pre-trained first model further comprises: obtaining a second
reward value corresponding to the drug recommendation information
for the patient; correspondingly, the method further comprises:
determining a reward function result based on the second reward
value and the evaluation result; and training the first model based
on the reward function result, until the training of the first
model is completed.
6. The drug recommendation method according to claim 2, further
comprising: acquiring the medical record information of the patient
to be trained and medical prescription data associated with the
medical record information of the patient to be trained from a
historical medical record database, wherein the medical
prescription data contains data of at least one drug; performing
vectorization processing on the medical record information of the
patient to be trained and the medical prescription data, to obtain
a medical record vector of the patient and at least one drug
vector; and obtaining the pre-trained first model based on the
medical record vector of the patient and the at least one drug
vector.
7. The drug recommendation method according to claim 1, wherein the
determining the drug recommendation information for the target
object based on the related information of the target object and
the first model comprises: performing word segmentation processing
on the related information of the target object, to obtain related
information after the word segmentation processing; performing
vectorization processing on the related information after the word
segmentation processing, to obtain vectorized related information;
and determining the drug recommendation information for the target
object based on the vectorized related information and the first
model.
8. A drug recommendation device, comprising: at least one
processor; and a memory communicatively connected to the at least
one processor, wherein the memory stores instructions executable by
the at least one processor, the instructions are executed by the at
least one processor to enable the at least one processor to:
acquire related information of a target object; and determine drug
recommendation information for the target object based on the
related information of the target object and a first model, wherein
the drug recommendation information contains information of at
least one drug, wherein the first model is a model obtained by
performing iterative processing on output information of a second
model, and the second model is used for evaluating drug
recommendation information output by the first model during the
iterative processing, to obtain an evaluation result of the drug
recommendation information.
9. The drug recommendation device according to claim 8, wherein the
instructions are executed by the at least one processor to further
enable the at least one processor to: obtain drug recommendation
information for a patient, based on medical record information of
the patient to be trained and a pre-trained first model; and
determine whether training of the first model is completed based on
an evaluation result; and obtain the evaluation result of the drug
recommendation information for the patient based on the second
model, wherein the evaluation result indicates whether there is
incompatibility in the drug recommendation information for the
patient.
10. The drug recommendation device according to claim 9, wherein
the instructions are executed by the at least one processor to
further enable the at least one processor to update the training of
the first model, to obtain an updated-trained first model, in
response to determining that the evaluation result indicates there
is incompatibility in the drug recommendation information for the
patient; re-obtain drug recommendation information for the patient
by using the updated-trained first model and the medical record
information of the patient to be trained; and determine that the
training of the first model is not completed, until the evaluation
result indicates that there is no incompatibility in the drug
recommendation information for the patient; and update training of
the second model, to obtain an updated-trained second model; and
re-obtain an evaluation result of the drug recommendation
information for the patient based on the updated-trained second
model.
11. The drug recommendation device according to claim 10, wherein
the instructions are executed by the at least one processor to
further enable the at least one processor to evaluate a drug
combination output by the pre-trained first model based on the
second model, to obtain a first reward value corresponding to the
drug combination and a probability value of incompatibility between
drugs in the drug combination, and take the first reward value and
the probability value as the evaluation result.
12. The drug recommendation device according to claim 11, wherein
the instructions are executed by the at least one processor to
further enable the at least one processor to obtain a second reward
value corresponding to the drug recommendation information for the
patient; determine a reward function result based on the second
reward value and the evaluation result; and train the first model
based on the reward function result, until the training of the
first model is completed.
13. The drug recommendation device according to claim 9, wherein
the instructions are executed by the at least one processor to
further enable the at least one processor to: acquire the medical
record information of the patient to be trained and medical
prescription data associated with the medical record information of
the patient to be trained from a historical medical record
database, wherein the medical prescription data contains data of at
least one drug; perform vectorization processing on the medical
record information of the patient to be trained and the medical
prescription data, to obtain a medical record vector of the patient
and at least one drug vector; and obtain the pre-trained first
model based on the medical record vector of the patient and the at
least one drug vector.
14. The drug recommendation device according to claim 8, wherein
the instructions are executed by the at least one processor to
further enable the at least one processor to perform word
segmentation processing on the related information of the target
object, to obtain related information after the word segmentation
processing; perform vectorization processing on the related
information after the word segmentation processing, to obtain
vectorized related information; and determine the drug
recommendation information for the target object based on the
vectorized related information and the first model.
15. A non-transitory computer-readable storage medium for storing
computer instructions, wherein the computer instructions, when
executed by a computer, cause the computer to acquire related
information of a target object; and determine drug recommendation
information for the target object based on the related information
of the target object and a first model, wherein the drug
recommendation information contains information of at least one
drug, wherein the first model is a model obtained by performing
iterative processing on output information of a second model, and
the second model is used for evaluating drug recommendation
information output by the first model during the iterative
processing, to obtain an evaluation result of the drug
recommendation information.
16. The non-transitory computer-readable storage medium according
to claim 15, wherein the computer instructions, when executed by a
computer, further cause the computer to: obtain drug recommendation
information for a patient, based on medical record information of
the patient to be trained and a pre-trained first model; obtain an
evaluation result of the drug recommendation information for the
patient based on the second model, wherein the evaluation result
indicates whether there is incompatibility in the drug
recommendation information for the patient; and determine whether
training of the first model is completed based on the evaluation
result.
17. The non-transitory computer-readable storage medium according
to claim 16, wherein the computer instructions, when executed by a
computer, further cause the computer to: update the training of the
first model and update training of the second model, to obtain an
updated-trained first model and an updated-trained second model, in
response to determining that the evaluation result indicates there
is incompatibility in the drug recommendation information for the
patient; re-obtain drug recommendation information for the patient
by using the updated-trained first model and the medical record
information of the patient to be trained, and re-obtain an
evaluation result of the drug recommendation information for the
patient based on the updated-trained second model; and determine
that the training of the first model is not completed, until the
evaluation result indicates that there is no incompatibility in the
drug recommendation information for the patient.
18. The non-transitory computer-readable storage medium according
to claim 17, wherein the computer instructions, when executed by a
computer, further cause the computer to: evaluate a drug
combination output by the pre-trained first model based on the
second model, to obtain a first reward value corresponding to the
drug combination and a probability value of incompatibility between
drugs in the drug combination, and take the first reward value and
the probability value as the evaluation result.
19. The non-transitory computer-readable storage medium according
to claim 18, wherein the obtaining the drug recommendation
information for the patient based on the medical record information
of the patient to be trained and the pre-trained first model
further comprises: obtaining a second reward value corresponding to
the drug recommendation information for the patient;
correspondingly, the computer instructions, when executed by a
computer, further cause the computer to: determine a reward
function result based on the second reward value and the evaluation
result; and train the first model based on the reward function
result, until the training of the first model is completed.
20. The non-transitory computer-readable storage medium according
to claim 16, wherein the computer instructions, when executed by a
computer, further cause the computer to: acquire the medical record
information of the patient to be trained and medical prescription
data associated with the medical record information of the patient
to be trained from a historical medical record database, wherein
the medical prescription data contains data of at least one drug;
perform vectorization processing on the medical record information
of the patient to be trained and the medical prescription data, to
obtain a medical record vector of the patient and at least one drug
vector; and obtain the pre-trained first model based on the medical
record vector of the patient and the at least one drug vector.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to Chinese patent
application, No. 202010589770.X, entitled "Drug Recommendation
Method and Device, Electronic Apparatus, and Storage Medium", filed
with the Chinese Patent Office on Jun. 24, 2020, which is hereby
incorporated by reference in its entirety.
TECHNICAL FIELD
[0002] The present application relates to a field of computer
technology, and in particular, to fields of artificial intelligence
deep learning technology, intelligent recommendation, and knowledge
graph.
BACKGROUND
[0003] In existing technology, in the era of information society,
every traditional field has been impacted by emerging technologies.
The technologies of machine learning and artificial intelligence
have achieved milestone breakthroughs in various fields. Nowadays,
with the advent of the era of big data and artificial intelligence,
more and more large companies and research institutions have begun
to enter the fields of Internet medical and intelligent drug
recommendation.
SUMMARY
[0004] A drug recommendation method and device, an electronic
apparatus, and a storage medium are provided in the present
application.
[0005] According to a first aspect of the present application, a
drug recommendation method is provided. The method includes:
[0006] acquiring related information of a target object; and
[0007] determining drug recommendation information for the target
object based on the related information of the target object and a
first model, wherein the drug recommendation information contains
information of at least one drug,
[0008] wherein the first model is a model obtained by performing
iterative processing on output information of a second model, and
the second model is used for evaluating drug recommendation
information output by the first model during the iterative
processing, to obtain an evaluation result of the drug
recommendation information.
[0009] According to a second aspect of the present application, a
drug recommendation device is provided. The device includes:
[0010] an information acquisition module, configured to acquire
related information of a target object; and
[0011] a drug recommendation module, configured to determine drug
recommendation information for the target object based on the
related information of the target object and a first model, wherein
the drug recommendation information contains information of at
least one drug,
[0012] wherein the first model is a model obtained by performing
iterative processing on output information of a second model, and
the second model is used for evaluating drug recommendation
information output by the first model during the iterative
processing, to obtain an evaluation result of the drug
recommendation information.
[0013] According to a third aspect of the present application, an
electronic apparatus is provided. The electronic apparatus
includes:
[0014] at least one processor; and
[0015] a memory communicatively connected to the at least one
processor, wherein
[0016] the memory stores instructions executable by the at least
one processor, the instructions are executed by the at least one
processor to enable the at least one processor to perform the
aforementioned method.
[0017] According to a fourth aspect of the present application, a
non-transitory computer-readable storage medium for storing
computer instructions is provided. The computer instructions, when
executed by a computer, cause the computer to perform the
aforementioned method.
[0018] By applying embodiments of the present application, a drug
recommendation is provided by a first model according to related
information of a target object. In addition, the first model is
obtained by performing iterative processing on output information
of a second model, and the role of the second model is to evaluate
drug recommendation information. In this way, since the evaluation
of drug recommendation information is introduced in training of the
first model, so that finally recommended drugs may be more
accurate.
[0019] It should be understood that the content described herein is
not intended to denote key or critical elements of embodiments of
the present application nor to limit the scope of the present
application. Further features of the present application may be
readily understood from the following description.
BRIEF DESCRIPTION OF THE DRAWINGS
[0020] The drawings are used to better understand the scheme and do
not constitute a limitation to the present application,
wherein:
[0021] FIG. 1 is a schematic flowchart showing a drug
recommendation method according to an embodiment of the present
application;
[0022] FIG. 2 is a schematic diagram showing a processing
architecture according to an embodiment of the present
application;
[0023] FIG. 3 is a schematic flowchart showing model pre-training
according to an embodiment of the present application;
[0024] FIG. 4 is a schematic diagram showing another processing
architecture according to an embodiment of the present
application;
[0025] FIG. 5 is a schematic flowchart showing model iteration
processing according to an embodiment of the present
application;
[0026] FIG. 6 is a first schematic diagram showing a composition
structure of a drug recommendation device according to an
embodiment of the present application;
[0027] FIG. 7 is a second schematic diagram showing a composition
structure of a drug recommendation device according to an
embodiment of the present application; and
[0028] FIG. 8 is a block diagram showing an electronic apparatus
for implementing a drug recommendation method according to an
embodiment of the present application.
DETAILED DESCRIPTION
[0029] The exemplary embodiments of the application will be
described below in combination with drawings, including various
details of the embodiments of the present application to facilitate
understanding, which should be considered as exemplary only.
Therefore, those of ordinary skill in the art should realize that
various changes and modifications can be made to the embodiments
described herein without departing from the scope and spirit of the
present application. Likewise, descriptions of well-known functions
and structures are omitted in the following description for clarity
and conciseness.
[0030] With the breakthrough of AlphaGo in the field of Go, more
and more fields adopt models and methods based on the reinforcement
learning. Especially, in consideration of the field of
human-computer interaction, the reinforcement learning has its
natural advantages. Regarding the medical intelligent drug
recommendation, on the one hand, when providing a drug
recommendation, effects of the symptomatic treatment and the
etiological treatment of recommended drugs should be taken into
account, and on the other hand, the incompatibility and interaction
between drugs are necessarily to be taken into account, while a
reinforcement learning model may enable a system to learn optimal
drug recommendation logic and accurately provide a drug
recommendation result. Therefore, in the present application, both
the accuracy and rationality of a drug recommendation combination
are taken into consideration, and how to provide intelligent drug
combination recommendation is transformed into an enhancement of
model training, to optimize a model. The present application
proposes an intelligent drug recommendation method according to
data of medical orders and medical records of a target object
(e.g., a patient or user), as well as data from medical
literatures, national pharmacopoeia, and massive drug
instructions.
[0031] Specifically, a drug recommendation method is provided
according to an embodiment of the present application. As shown in
FIG. 1, the method includes:
[0032] S101: acquiring related information of a target object;
[0033] S102: determining drug recommendation information for the
target object based on the related information of the target object
and a first model, wherein the drug recommendation information
contains information of at least one drug,
[0034] wherein the first model is a model obtained by performing
iterative processing on output information of a second model, and
the second model is used for evaluating drug recommendation
information output by the first model during the iterative
processing, to obtain an evaluation result of the drug
recommendation information.
[0035] The above scheme relates to the fields of artificial
intelligence deep learning technology, intelligent recommendation,
and knowledge graph. In addition, the above scheme of the
embodiment may be applied to an electronic apparatus, such as a
terminal device, a personal computer (PC), a notebook computer, and
so on. The above scheme may also be applied to a server, and if so,
the acquired related information of the target object may be
related information of the target object received from the terminal
device. In addition, after being obtained by processing, the final
drug recommendation information may be stored, and the drug
recommendation information may further be pushed to a terminal
device for displaying thereby.
[0036] During a determination of drug recommendation using a
trained first model, in S101, the target object may be any user or
patient. The related information of the target object may be at
least one of symptom information, complaint information, diagnosis
result information, and medical record information of a user, i.e.,
the target object.
[0037] Correspondingly, in S102, determining drug recommendation
information for the target object based on the related information
of the target object and a first model includes:
[0038] performing word segmentation processing on the related
information of the target object, to obtain related information
after the word segmentation processing;
[0039] performing vectorization processing on the related
information after the word segmentation processing, to obtain
vectorized related information; and
[0040] determining the drug recommendation information for the
target object based on the vectorized related information and the
first model.
[0041] Specifically, the word segmentation processing may be
performed on the related information of the target object using a
medical entity database. Being different from a word segmentation
for a traditional text, since in the present application, a text is
for the medical field, a dedicated medical entity database is
required to be established first, and then a sentence segmentation
is performed based on the medical entity database, to achieve a
medical entity word segmentation, so that a sentence may be
segmented into respective words, while medical-related words will
be marked with respective categories, e.g., "vomiting" will be
marked as "symptom", and "pneumonia" will be marked as "disease".
The word segmentation processing here may be understood as
filtering from a large number of input words, to obtain information
such as symptom related to a target object (user or patient).
[0042] Vectorization processing is performed on the related
information obtained after the word segmentation processing, to
obtain vectorized related information, that is, after the word
segmentation, a word vectorization technology (Word2Vec, GloVe) is
adopted, to map words into vector expressions. Finally, the related
information of a patient, such as a symptom, is expressed as Vemr
(e1, e2, . . . , en), where ei denotes a vectorized representation
of a medical entity.
[0043] An exemplary description of the above processing is provided
in conjunction with FIG. 2. Acquiring related information of a
target object refers to acquiring at least one of a main complaint,
a medical record, a diagnosis, etc. of any patient who currently
needs to acquire a drug recommendation. A medical entity word
segmentation processing is performed on the related information of
the patient, to obtain related information of the patient after a
word segmentation processing. A word vectorization processing is
performed based on the related information of the patient after the
word segmentation processing, to obtain vectorized information of
the medical record of the patient. The vectorized information of
the medical record of the patient is input into a first model, so
that drug recommendation information, i.e., a recommended drug
combination, for the target object, i.e., a patient is
determined.
[0044] It is also pointed out in S102 that the first model is a
model obtained by performing iterative processing on output
information of a second model. The second model is used for
evaluating drug recommendation information output by the first
model during the iterative processing, to obtain an evaluation
result of the drug recommendation information. That is, in
embodiments of the present application, Actor-Critic reinforcement
learning model is used to perform the model iterative processing,
and the first model finally obtained may output an optimal drug
combination, in which no incompatibility exists between drugs.
[0045] The iterative processing mainly includes two parts, i.e.,
Actor and Critic, where Actor is used for implementing a drug
recommendation strategy, and Critic is used for evaluating a
rationality of the medication strategy.
[0046] The first part relates to an Actor model based on
reinforcement learning, i.e., the first model. A drug
recommendation is performed by training a deep semantic ranking
model as the first model.
[0047] The second part relates to a Critic model based on
reinforcement learning, i.e., the second model. The Critic model in
the reinforcement learning network, i.e., the second model is
obtained by training another deep network model in conjunction with
knowledge graphs. The second model is used to evaluate a drug
combination recommended by a current Actor, to obtain an evaluation
result (that is, if there is a negative relationship or
incompatibility between drugs, the Critic will give a negative
reward).
[0048] The first model may be the Actor model based on
reinforcement learning. In addition, the first model (or referred
to as the Actor model) may be constructed based on a deep semantic
ranking network BERT model, and it is a model for ranking the drug
recommendations. The BERT-based deep learning method is widely used
in various fields of natural language processing, including
automatic text classification, sentiment analysis and machine
translation, etc., which will not be described in detail here.
[0049] In an example, a pre-training procedure of the first model,
as shown in FIG. 3, may include:
[0050] S201: acquiring the medical record information of the
patient to be trained and medical prescription data associated with
the medical record information of the patient to be trained from a
historical medical record database, wherein the medical
prescription data contains data of at least one drug;
[0051] S202: performing vectorization processing on the medical
record information of the patient to be trained and the medical
prescription data, to obtain a medical record vector of the patient
and at least one drug vector;
[0052] S203: obtaining the pre-trained first model based on the
medical record vector of the patient and the at least one drug
vector.
[0053] In the pre-training or construction of the first model,
learning is made according to a large amount of data of medical
orders and medical records based on a deep semantic ranking model
(BERT). In the training process, a ranking binary model is
constructed based on Electronic Medical Record (EMR) information of
the patient and medically ordered drugs (i.e., the first model is
constructed or trained).
[0054] In S201, a historical medical record database may be
understood as a sample database, and the medical record information
of the patient to be trained may be EMR information of any patient
in the historical medical record database. It should be understood
that there may be one or more medical record information of the
patient to be trained in the historical medical record database. In
the procedure of pre-training the first model, different medical
record information of the patient to be trained may be extracted
multiple times for each training processing, until the position of
the pre-trained first model is obtained. Since the processing
procedure of pre-training the first model according to different
medical record information of the patient to be trained is the same
each time, the description thereto will not be repeated.
[0055] In S202, the performing vectorization processing on the
medical record information of the patient to be trained and the
medical prescription data, to obtain a medical record vector of the
patient and at least one drug vector may specifically include:
[0056] performing word segmentation processing on the medical
record information of the patient to be trained, to obtain
medical-related segmented words of the patient; and then performing
vectorization processing based on the medical-related segmented
words of the patient after the word segmentation processing, to
obtain the medical record vector of the patient;
[0057] performing vectorization processing on the medical
prescription data to be trained, to obtain at least one drug
vector,
[0058] wherein the performing vectorization processing on the
medical prescription data to be trained may further include:
performing word segmentation processing on the medical prescription
data, to obtain medical prescription segmented words; and then
performing vectorization processing on the medical prescription
segmented words.
[0059] It should be pointed out that the above vectorization
processing on the medical prescription data may be performed at the
same time as or after the vectorization processing is performed
based on the medical-related segmented words of the patient after
the word segmentation processing. Related types of word
segmentation are executed after vectorization processing. Of
course, the vectorization processing may also be performed before
the vectorization processing is performed based on the
medical-related segmented words after the word segmentation
processing, as long as at least one drug vector may be obtained,
and the processing sequences is not limited in the procedure.
[0060] Specifically, vectorization processing is firstly performed
on content of the EMR information of a patient to be trained (which
may include main complaint+current medical history+diagnosis,
etc.). Unlike a word segmentation of a traditional text, in the
present application, here is for a text in the medical field, so a
dedicated medical entity database is established at first, and then
a sentence segmentation is performed based on the medical entity
database, to obtain medical entity segmented words. Thus, a
sentence may be segmented into respective words, while
medical-related words may be marked with respective categories. For
example, "vomiting" will be marked as "symptom" and "pneumonia"
will be marked as "disease". The word segmentation processing here
may be understood as filtering from a large number of input words,
to obtain medical record information of a patient to be trained,
which is related to the symptom and other information of the
patient to be trained.
[0061] After a word segmentation, words are mapped into expression
of vectors using a word vectorization technology (Word2Vec, GloVe).
Finally, the patient's EMR is expressed as Vemr (e1, e2, . . . ,
en), where ei denotes a vectorized representation of a medical
entity. In the same way, that is, by using the word vectorization
processing, medical order prescription data corresponding to the
EMR is parsed into vectors of a plurality of drugs (e.g., aspirin,
ribavirin, etc.), i.e., Vdrug (d1, d2, . . . dn), where di
represents a vector expression of a drug.
[0062] In S203, regarding the construction of the first model, a
deep learning framework may be used to construct a pairwise-based
BERT deep semantic ranking model.
[0063] As compared with a traditional neural network (DNN, RNN)
framework, on the one hand, the BERT-based deep network model
considers an order relationship between words in a sentence, which
is more in line with the basic assumptions of natural language
processing (the word order influences the semantic expression), and
on the other hand, the BERT is implemented internally based on the
Transformer structure, with a self-attention mechanism, which
considers a relationship between patient entity word information.
Therefore, in a preferred example provided by embodiments of the
present application, the BERT model is used to construct the first
model. However, in actual processing, it is not excluded that other
models may also be used to construct the first model of the present
application, which are not exhaustively repeated here.
[0064] Further, the BERT model (i.e., the first model or so called
Actor model) is based on patient's EMR vectors and corresponding
medical order prescription vectors as inputs, and outputs a
priority of a single combination, and a medical combination finally
takes the drugs of topk.
[0065] In the construction of the first model, for a patient i, it
may be set that the positive sample is the patient information
state Spos=<Viemr, dk.di-elect cons.Vidrug>, and the
information state of the negative sample is Sneg=<Viemr,
dkVidrug>. In addition, the reward of the positive sample is
reward=+ra, and the reward of the negative samples is
reward=-ra.
[0066] The negative sample is constructed, based on the patient's
EMR, by randomly acquiring drugs not in the medical order of the
EMR. The rewards of the positive and negative sample may be
understood as labels of the positive and negative samples, and may
be preset according to actual conditions. The value of ra may also
be preset according to actual conditions, such as 1.
[0067] After the Actor model has been pre-trained, a strategy Q
(Semr, Adrug) according to the patient's recommended drug
combinations may be obtained, and an optimal Q* (Semr, Adrug) may
be obtained on a training set, where the method of selecting an
optimal drug combination may be based on a policy gradient, which
will not be described in detail here.
[0068] In an example, the construction or pre-training of the first
model is described with reference to FIG. 4. Based on a historical
medical record database, i.e., an EMR medical record database in
FIG. 4, medical record information of a certain patient to be
trained and corresponding medical prescription data are obtained,
where the medical record data of the patient may include at least
one of a main complaint, a medical history, a diagnosis and other
information of the patient shown in FIG. 4, and the medical
prescription data is that shown in FIG. 4.
[0069] The medical entity vectorization processing is performed on
the medical record data of the patient, to obtain medical record
data of the patient after the vectorization processing, i.e., the
medical record vector of the patient obtained by performing the
word vectorization processing on the patient's EMR. The medical
prescription data is processed in the same way, for example,
including the vectorization processing based on a medical entity
and the word vectorization processing on a medical prescription,
and finally at least one drug vector is obtained.
[0070] The first model is pre-trained based on the medical record
vector of the patient and at least one drug vector. The pre-trained
first model is obtained by repeating the above steps. Here, whether
the pre-training of the first model is completed may be determined
according to a back propagation of a corresponding loss function.
The design of the loss function corresponding to the first model is
not described in detail here.
[0071] After the pre-training of the aforementioned first model,
i.e., the Actor model, is completed, iterative processing may be
performed on the first model based on a second model, to obtain a
finally applicable first model through reinforcement learning.
[0072] Based on the pre-trained deep semantic ranking model (the
first model or the so called Actor model), when a system acquires a
patient's diagnosis and clinical manifestation as well as crowd
information, a reinforcement learning-based first model (or Actor
module) completes a drug combination recommendation based on the
patient's EMR information. At the same time, a reinforcement
learning-based second model (or so called Critic model or module)
evaluates a current drug combination recommendation, to determine
whether the current drug combination have any drug incompatibility
or any negative relationship between drugs, and feeds an obtained
reward (+rc, -rc) back to the reinforcement learning agent. If the
drug combinations recommended by the reinforcement learning-based
Actor module are incompatible, the second model (or referred as the
Critic model or module) will give a negative reward, to prompt the
reinforcement learning agent to update the strategy function. The
iteration is performed, until a final drug combination
recommendation given by the Actor has no drug incompatibility under
the evaluation by the Critic module.
[0073] The reinforcement learning agent may be composed of the
above first model and the second model.
[0074] Experiments have proved that according to the reinforcement
learning-based intelligent consultation method, interaction between
drugs represented through the reward function during a training
procedure may be taken into account, so that the drug
incompatibility in a combined drug recommendation is avoided, and
the probability of side effects between drug combinations is
reduced, while the symptomatic treatment and etiological treatment
are ensured, in this way, the drug recommendation effect may better
meet a doctors' expectation.
[0075] Specifically, in an example, the reinforcement learning of
the first model and the second model, i.e., the iterative
processing, may be as shown in FIG. 5, including:
[0076] S301: obtaining drug recommendation information for a
patient, based on medical record information of the patient to be
trained and the pre-trained first model;
[0077] S302: obtaining an evaluation result of the drug
recommendation information for the patient based on the second
model, wherein the evaluation result indicates whether there is
incompatibility in the drug recommendation information for the
patient;
[0078] S303: determining whether training of the first model is
completed based on the evaluation result.
[0079] In S301, in the medical record information of the patient to
be trained, only medical record data of the patient is included,
such as at least one of a symptom, a main complaint, a medical
record, and a diagnosis result of the patient. That is, after a
first model is successfully constructed, a second model may be
employed to further adjust a weight (or feature parameter) in the
first model. At this time, the input of the first model is the
medical record data of the patient. The output of the first model
is the drug recommendation information for the patient.
[0080] In a traditional intelligent drug recommendation method, the
rationality of drug combinations (the relationship between drugs
and the incompatibility of drugs) are not restricted. Although
generated drug combinations may treat symptomatically, there may be
drug incompatibility. Therefore, in the present application, a
second model, i.e., a reinforcement learning-based Critic module
(or model) is provided for further constraints. In S302, it can be
understood that a second model (Critic module) is trained to
evaluate the drug rationality, i.e., the rationality of a drug
combination is scored, to acquire an additional reward (rc).
[0081] In S302, obtaining an evaluation result of the drug
recommendation information for the patient based on the second
model includes:
[0082] evaluating a drug combination output by the pre-trained
first model based on the second model, to obtain a first reward
value corresponding to the drug combination and a probability value
of incompatibility between drugs in the drug combination; and
taking the first reward value and the probability value as the
evaluation result.
[0083] Specifically, the second model acquires a knowledge graph G
(di, dj) representing drug interactions in conjunction with a rule,
to provide a drug score as the aforementioned evaluation result.
The second model (Critic module) may determine whether drugs have
incompatibility relationships by leaning through a neural network
(e.g., DNN) based on relationships constructed by the graph G of
drug relationship, which may also be a drug relationship matrix
G.
[0084] In the drug relationship matrix G, if there is
incompatibility between drugs i and j, then G (di, dj)=-a,
otherwise G (di, dj)=a, where a is a settable reward value, such as
1; of course, it may also be set to 0.5, 2 and so on according to
actual situations, which is not exhaustively repeated here.
[0085] In the reinforcement learning training stage, the second
model combines the reward value obtained from the evaluation of the
drug output of the first model and the probability of the DNN, to
give a reward (rc)=G (di, dj)+rp,
[0086] where rp is a probability of drug incompatibility determined
by the Critic, and where rp=DNN (Adrug).
[0087] In S303, determining whether training of the first model is
completed based on the evaluation result includes:
[0088] updating the training of the first model and updating
training of the second model, to obtain an updated-trained first
model and an updated-trained second model, in response to
determining that the evaluation result indicates there is
incompatibility in the drug recommendation information for the
patient;
[0089] re-obtaining drug recommendation information for the patient
by using the updated-trained first model and the medical record
information of the patient to be trained, and re-obtaining an
evaluation result of the drug recommendation information for the
patient based on the updated-trained second model;
[0090] determining that the training of the first model is not
completed, until the evaluation result indicates that there is no
incompatibility in the drug recommendation information for the
patient.
[0091] In other words, if an evaluation result of the second model
indicates that there is incompatibility in the drug recommendation
information, the first model needs to be adjusted again, so that
the first model may output drug recommendation information having
no drug incompatibility, while the second model will also be
adjusted accordingly.
[0092] It should be understood here that the medical record
information of the patient to be trained input into the first model
after the updated training and the medical record information of
the patient to be trained in S301 may be the same as or different
from each other, and they both may be understood as being acquired
from the historical medical record database.
[0093] In another example, the reinforcement learning of the first
model and the second model, i.e., the iterative processing, is also
described with reference to FIG. 4. After the pre-training is
completed, input of the first model for reinforcement learning may
be medical record information of the patient to be trained. Here,
the medical record information of the patient to be trained may
only include medical record vector of the patient after a word
vectorization is performed, i.e., the left branch divided from the
EMR medical record database shown in FIG. 4.
[0094] Drug recommendation information for the patient is obtained
according to the first model and the medical record vector of the
patient, the drug recommendation information including a drug
combination composed of at least one drug.
[0095] An evaluation result of the drug recommendation information
for the patient is obtained based on the second model, where the
evaluation result indicates whether there is incompatibility in the
drug recommendation information for the patient.
[0096] The training of the first model is updated, in response to
determining that the evaluation result indicates there is
incompatibility in the drug recommendation information for the
patient. In this case, the method may further include updating the
training of the second model based on the updated-trained first
model.
[0097] The drug recommendation information for the patient is
re-obtained by using the updated-trained first model and the
medical record information of the patient to be trained, and an
evaluation result of the drug recommendation information for the
patient is re-obtained based on the updated-trained second
model.
[0098] The training of the first model is determined being not
completed, until the evaluation result indicates that there is no
incompatibility in the drug recommendation information for the
patient.
[0099] Further, the obtaining drug recommendation information for a
patient, based on medical record information of the patient to be
trained and a pre-trained first model further includes: obtaining a
second reward value corresponding to the drug recommendation
information for the patient;
[0100] correspondingly, the method further includes:
[0101] determining a reward function result based on the second
reward value and the evaluation result;
[0102] training the first model based on the reward function
result, until the training of the first model is completed.
[0103] Specifically, a final evaluation result (reward (rc)) of the
reinforcement learning-based second model and the second reward
value (represented as reward (ra)) of the first model are weighted,
as a final reward function result (Rf=rc+ra).
[0104] In the above processing, through mutual iterations of the
reinforcement learning-based Actor module and Critic module and
based on the final reward Rf, an optimal Q*F (Semr, Adrug) is
generated, thereby providing a result of an optimal intelligent
drug combination recommendation.
[0105] The method of the present application may be applied to many
scenarios, including, but not limited to, clinical decision-making
assistance system prescription recommendation, intelligent drug
recommendation assistant, medical rational drug use and solution
pharmacist prescription teaching, and for teaching interns how to
prescribe according to patient's situation, etc.
[0106] Being different from a commodity recommendation in the
e-commerce field, as to the medical intelligent drug
recommendation, not only drugs are required to be recommended
according to a diagnosis and clinical manifestation of a known
patient, the relationships between drugs and situations of drugs
and patients are also required to be taken into account. A series
of information such as the main compliant, the medical history, the
allergy history, etc. of a patient is required to be
comprehensively considered, to provide a most appropriate
prescription drug recommendation. This is difficult to be achieved
in the whole field of intelligent medical, however, it is
important. An idealized intelligent consultation method should not
only ensure that recommended drugs may symptomatically treat
patient's clinical manifestation, but also be able to etiologically
treat according to the diagnosis. In addition, when a drug
recommendation is provided, the incompatibility between and the
side effects of the drugs are required to still be taken into
consideration. However, these are often not easy to be satisfied at
the same time.
[0107] In the related technology, the medical record mining method
based on big data deep learning and the drug recommendation method
based on the probabilistic graph of the drug knowledge graph cannot
provide satisfactory results. On the one hand, as to a method based
on big data deep learning (CNN, LSTM, BERT, etc.), a model may
provide symptomatic treatment and etiological treatment when there
is enough medical order data in medical records, and medical order
data may be completely correct. However, under normal
circumstances, it is impossible to acquire a large amount of
medical order data, and the quality of medical order data varies
from hospital to hospital, while drugs recommended by the medical
record mining method based on big data deep learning may be
incompatible, which will cause very serious problems in actual use.
On the other hand, the method based on the probabilistic graph
model (PGM) of the knowledge graph may conduct an effective
consultation through a transition probability matrix of
drug-indication (diagnosis+clinical manifestation), however, it
often has a high computational complexity in the model inference.
Meanwhile, the drug-indication transition matrix of the
probabilistic graph model usually needs to be marked by a
professional doctor, or mined by means of human-computer
cooperation (graph mining+artificial iterative evaluation). This
kind of expert system-like model faces great challenges in the
expansion of different hospitals and diverse patient situations.
Therefore, a new method is needed to make breakthroughs in the
field of intelligent drug recommendation.
[0108] By applying schemes of the present application, a drug
recommendation may be made based on a first model according to
related information of a target object. In addition, the first
model is obtained by performing iterative processing on output
information of a second model, and the role of the second model is
to evaluate drug recommendation information. In this way, since an
evaluation of drug recommendation information is introduced in the
training of the first model, finally recommended drugs may be more
accurate.
[0109] In addition, according to schemes provided by the present
application, the first model is trained (or enhanced) by
introducing an evaluation result indicating whether there is
incompatibility between drugs in the iterative processing. Further,
according to schemes provided by the present application,
artificial intervention may be avoided as much as possible in the
processing of model training and reinforcement learning, thereby
ultimately ensuring that drugs recommended by a first model is an
optimal solution that may avoid incompatibility.
[0110] According to an embodiment of the present application, a
drug recommendation device is provided as shown in FIG. 6. The
device includes:
[0111] an information acquisition module 61, configured to acquire
related information of a target object;
[0112] a drug recommendation module 62, configured to determine
drug recommendation information for the target object based on the
related information of the target object and a first model, wherein
the drug recommendation information contains information of at
least one drug;
[0113] wherein the first model is a model obtained by performing
iterative processing on output information of a second model, and
the second model is used for evaluating drug recommendation
information output by the first model during the iterative
processing, to obtain an evaluation result of the drug
recommendation information.
[0114] As shown in FIG. 7, the device further includes:
[0115] a first module 63, configured to obtain drug recommendation
information for a patient, based on medical record information of
the patient to be trained and a pre-trained first model; and
determine whether training of the first model is completed based on
an evaluation result; and
[0116] a second module 64, configured to obtain the evaluation
result of the drug recommendation information for the patient based
on the second model, wherein the evaluation result indicates
whether there is incompatibility in the drug recommendation
information for the patient.
[0117] The first module 63 is configured to update the training of
the first model, to obtain an updated trained first model, in
response to determining that the evaluation result indicates there
is incompatibility in the drug recommendation information for the
patient; re-obtain drug recommendation information for the patient
by using the updated-trained first model and the medical record
information of the patient to be trained; and determine that the
training of the first model is not completed, until the evaluation
result indicates that there is no incompatibility in the drug
recommendation information for the patient.
[0118] The second module 64 is configured to update training of the
second model, to obtain an updated-trained second model; and
re-obtain an evaluation result of the drug recommendation
information for the patient based on the updated-trained second
model.
[0119] The second module 64 is configured to evaluate a drug
combination output by the pre-trained first model based on the
second model, to obtain a first reward value corresponding to the
drug combination and a probability value of incompatibility between
drugs in the drug combination, and take the first reward value and
the probability value as the evaluation result.
[0120] The first module 63 is configured to obtain a second reward
value corresponding to the drug recommendation information for the
patient; determine a reward function result based on the second
reward value and the evaluation result; and train the first model
based on the reward function result, until the training of the
first model is completed.
[0121] The device further includes:
[0122] a pre-training module 65, configured to acquire the medical
record information of the patient to be trained and medical
prescription data associated with the medical record information of
the patient to be trained from a historical medical record
database, wherein the medical prescription data contains data of at
least one drug; perform vectorization processing on the medical
record information of the patient to be trained and the medical
prescription data, to obtain a medical record vector of the patient
and at least one drug vector; and obtain the pre-trained first
model based on the medical record vector of the patient and the at
least one drug vector.
[0123] The drug recommendation module 62, is configured to perform
word segmentation processing on the related information of the
target object, to obtain related information after the word
segmentation processing; perform vectorization processing on the
related information after the word segmentation processing, to
obtain vectorized related information; and determine the drug
recommendation information for the target object based on the
vectorized related information and the first model.
[0124] It should be pointed out here that the first module may send
or save a first model into a drug recommendation module, after the
training of the first model has been completed, so that the drug
recommendation module may perform subsequent drug recommendation
processing.
[0125] For functions of modules in drug recommendation devices
according to embodiments of the present application, reference may
be made to corresponding descriptions of the above method, and thus
a detailed description thereof is omitted herein.
[0126] According to an embodiment of the present application, an
electronic apparatus and a readable storage medium are provided in
the present application.
[0127] As shown in FIG. 8, it is a block diagram showing an
electronic apparatus applied with a drug recommendation method
according to an embodiment of the present application. The
electronic apparatus is intended to represent various forms of
digital computers, such as laptop computers, desktop computers,
workbenches, personal digital assistants, servers, blade servers,
mainframe computers, and other suitable computers. The electronic
apparatus may also represent various forms of mobile devices, such
as personal digital processors, cellular phones, intelligent
phones, wearable devices, and other similar computing devices.
Components shown in the present application, their connections and
relations, and their functions are merely examples, and are not
intended to limit the implementation of the application described
and/or required herein.
[0128] As shown in FIG. 8, the electronic apparatus includes: one
or more processors 801, a memory 802, and interfaces for connecting
various components, including a high-speed interface and a
low-speed interface. The various components are interconnected
using different buses and may be mounted on a common motherboard or
otherwise installed as required. The processor may process
instructions executed within the electronic apparatus, including
instructions for storing in or on a memory, to display graphical
information of a Graphical User Interface (GUI) on an external
input/output device (such as a display device coupled to the
interface). In other implementations, multiple processors and/or
multiple buses may be used with multiple memories and multiple
memories, if desired. Similarly, multiple electronic apparatuses
may be connected, each apparatus providing some of the necessary
operations (for example, as a server array, a group of blade
servers, or a multiprocessor system). In FIG. 8, one processor 801
is shown as an example.
[0129] The memory 802 is a non-transitory computer-readable storage
medium provided in the present application. The memory stores
instructions executable by at least one processor, so that the at
least one processor executes a drug recommendation method provided
in the present application. The non-transitory computer-readable
storage medium of the present application stores computer
instructions, which are configured to enable a computer to execute
a drug recommendation method provided in the present
application.
[0130] As a non-transitory computer-readable storage medium, the
memory 802 may be used to store non-transitory software programs,
non-transitory computer executable programs, and modules, such as
program instructions/modules corresponding to a drug recommendation
method in embodiments of the present application (e.g., the
information acquisition module, the drug recommendation module, the
first module, the second module and the pre-training module shown
in FIG. 7). The processor 801 executes various functional
applications and data processing of the server by running
non-transitory software programs, instructions, and modules stored
in the memory 802, that is, to implement an above drug
recommendation method in foregoing method embodiments.
[0131] The memory 802 may include a storage program area and a
storage data area, where the storage program area may be used to
store an application program required by an operating system or for
at least one function; the storage data area may be used to store
data created according to the use of an electronic apparatus. In
addition, the memory 802 may include a high-speed random-access
memory, and may also include a non-transitory memory, such as at
least one magnetic disk storage device, a flash memory device, or
other non-transitory solid-state storage device. In some
embodiments, the memory 802 may optionally include a memory set
remotely relative to the processor 801, and these remote memories
may be connected to the electronic apparatus through a network.
Examples of the above network include, but are not limited to, an
Internet, an intranet, a local area network, a mobile communication
network, and combinations thereof.
[0132] The electronic apparatus applied with a drug recommendation
method may further include an input device 803 and an output device
804. The processor 801, the memory 802, the input device 803, and
the output device 804 may be connected through a bus or in other
manners. In FIG. 8, a connection through a bus is shown as an
example.
[0133] The input device 803 may receive input numeric or character
information, and generate key signal inputs related to a user
setting and a function control of an electronic apparatus for
analyzing a search result applied with a webpage rendering method,
such as a touch screen, a keypad, a mouse, a trackpad, a touchpad,
a pointing stick, one or more mouse buttons, a trackball, a
joystick and other input devices. The output device 804 may include
a display device, an auxiliary lighting device (for example, an
LED), a haptic feedback device (for example, a vibration motor),
and the like. The display device may include, but is not limited
to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED)
display, and a plasma display. In some implementations, the display
device may be a touch screen.
[0134] Various implementations of the systems and technologies
described herein may be implemented in a digital electronic circuit
system, an integrated circuit system, an application specific
integrated circuit (ASIC), a computer hardware, a firmware, a
software, and/or combinations thereof. These various
implementations may include: implementations in one or more
computer programs, where the one or more computer programs are
executable and/or interpretable on a programmable system including
at least one programmable processor, programmable processor, where
the programmable processor may be a dedicated or general-purpose
programmable processor that may receive data and instructions from
a storage system, at least one input device, and at least one
output device, and may transmit the data and instructions to the
storage system, the at least one input device, and the at least one
output device.
[0135] These computing programs (also known as programs, software,
software applications, or codes) include machine instructions of a
programmable processor and may be implemented by using a high-level
procedural and/or object-oriented programming language, and/or an
assembly/machine language. As used herein, the terms
"machine-readable medium" and "computer-readable medium" refer to
any computer program product, apparatus, and/or device used to
provide machine instructions and/or data to a programmable
processor (for example, a magnetic disk, an optical disk, a memory,
and a programmable logic device (PLD)), including machine-readable
media that receives machine instructions as machine-readable
signals. The term "machine-readable signal" refers to any signal
used to provide machine instructions and/or data to a programmable
processor.
[0136] In order to provide an interaction with a user, systems and
techniques described herein may be implemented on a computer, where
the computer includes: a display device (for example, a Cathode Ray
Tube (CRT) or liquid crystal display (LCD) monitor) for displaying
information to a user; and a keyboard and pointing device (such as
a mouse or a trackball) through which a user may provide input to a
computer. Other kinds of devices may also be used to provide
interaction with a user. For example, a feedback provided to a user
may be a sensory feedback in any form (for example, a visual
feedback, an auditory feedback, or a haptic feedback), and a user
input (including an acoustic input, a voice input, or a tactile
input) may be received in any form.
[0137] The systems and technologies described herein may be
implemented in a computing system including a background component
(for example, as a data server), a computing system including a
middleware component (for example, an application server), or a
computing system including a front-end component (for example, a
user computer with a graphical user interface or a web browser,
through which the user may interact with an implementation of the
systems and technologies described herein), or a computer system
including any combination of such a background component, a
middleware component, or a front-end component. The components of
the system may be interconnected by any form or medium of digital
data communication (such as, a communication network). Examples of
a communication network include a Local Area Network (LAN), a Wide
Area Network (WAN), and the Internet.
[0138] It should be understood the steps in the various processes
described above may be reordered or omitted, or other steps may be
added therein. For example, the steps described in the application
may be performed parallelly, sequentially, or in different orders,
as long as the desired results of the technical solutions disclosed
in the application may be achieved, to which no limitation is made
herein.
[0139] Embodiments of the present application relate to fields of
artificial intelligence deep learning technology, intelligent
recommendation, and knowledge graphs. According to the technical
schemes of embodiments of the present application, a drug
recommendation is made based on a first model according to related
information of a target object. The first model is obtained by
performing iterative processing on output information of a second
model, where the role of the second model is to evaluate drug
recommendation information. In this way, since the evaluation of
drug recommendation information is introduced in training of the
first model, so that finally recommended drugs may be more
accurate.
[0140] It should be understood the steps in the various processes
described above may be reordered or omitted, or other steps may be
added therein. For example, the steps described in the application
may be performed parallelly, sequentially, or in different orders,
as long as the desired results of the technical solutions disclosed
in the application may be achieved, to which no limitation is made
herein.
[0141] The embodiments above do not constitute a limitation on the
protection scope of the present application. It should be
understood by those skilled in the art that various modifications,
combinations, sub-combinations, and substitutions may be available
according to design requirements and other factors. Any
modifications, equivalent replacements and improvements made within
the spirit and principle of the present application shall be
covered within the protection scope of the present application.
* * * * *